Enhancing Oncology-Specific Question Answering With Large Language Models Through Fine-Tuned Embeddings With Synthetic Data

Our findings highlight the effectiveness of pretrained contextual embeddings and sentence transformers in retrieving pertinent notes from oncology EHRs. The innovative use of LLM-synthesized query-passage pairs for data augmentation was proven to be effective. This fine-tuning approach holds significant promise in specialized fields like health care, where acquiring annotated data is challenging.